Overview

Dataset statistics

Number of variables18
Number of observations45528
Missing cells103
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 MiB
Average record size in memory144.0 B

Variable types

Numeric9
Categorical7
Boolean2

Alerts

customer_id has a high cardinality: 45528 distinct values High cardinality
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
prev_defaults is highly correlated with default_in_last_6months and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with prev_defaults and 1 other fieldsHigh correlation
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
credit_score is highly correlated with credit_card_defaultHigh correlation
prev_defaults is highly correlated with default_in_last_6months and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with credit_score and 2 other fieldsHigh correlation
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
prev_defaults is highly correlated with default_in_last_6months and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with prev_defaults and 1 other fieldsHigh correlation
prev_defaults is highly correlated with credit_card_default and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with credit_card_default and 1 other fieldsHigh correlation
gender is highly correlated with occupation_typeHigh correlation
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
no_of_days_employed is highly correlated with occupation_typeHigh correlation
occupation_type is highly correlated with gender and 1 other fieldsHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
credit_limit_used(%) is highly correlated with credit_card_defaultHigh correlation
credit_score is highly correlated with prev_defaults and 2 other fieldsHigh correlation
prev_defaults is highly correlated with credit_score and 2 other fieldsHigh correlation
default_in_last_6months is highly correlated with credit_score and 2 other fieldsHigh correlation
credit_card_default is highly correlated with credit_limit_used(%) and 3 other fieldsHigh correlation
net_yearly_income is highly skewed (γ1 = 203.6835038) Skewed
credit_limit is highly skewed (γ1 = 200.3871671) Skewed
customer_id is uniformly distributed Uniform
customer_id has unique values Unique
no_of_children has 32015 (70.3%) zeros Zeros

Reproduction

Analysis started2023-01-05 05:53:02.150888
Analysis finished2023-01-05 05:53:15.100795
Duration12.95 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

yearly_debt_payments
Real number (ℝ≥0)

Distinct45251
Distinct (%)99.6%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean31796.96531
Minimum2237.47
Maximum328112.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:15.146471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2237.47
5-th percentile10354.064
Q119231.14
median29081.65
Q340561.15
95-th percentile62796.392
Maximum328112.86
Range325875.39
Interquartile range (IQR)21330.01

Descriptive statistics

Standard deviation17269.72723
Coefficient of variation (CV)0.5431250141
Kurtosis9.772470795
Mean31796.96531
Median Absolute Deviation (MAD)10514.01
Skewness1.721201453
Sum1444631525
Variance298243478.7
MonotonicityNot monotonic
2023-01-04T21:53:15.221590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23213.372
 
< 0.1%
14243.922
 
< 0.1%
42710.332
 
< 0.1%
31606.522
 
< 0.1%
22534.212
 
< 0.1%
30683.362
 
< 0.1%
39822.632
 
< 0.1%
25862.022
 
< 0.1%
38694.12
 
< 0.1%
17794.722
 
< 0.1%
Other values (45241)45413
99.7%
(Missing)95
 
0.2%
ValueCountFrequency (%)
2237.471
< 0.1%
2752.111
< 0.1%
2881.981
< 0.1%
3139.061
< 0.1%
3230.671
< 0.1%
3256.331
< 0.1%
3324.481
< 0.1%
3328.381
< 0.1%
3369.881
< 0.1%
3506.051
< 0.1%
ValueCountFrequency (%)
328112.861
< 0.1%
279269.561
< 0.1%
276512.941
< 0.1%
274939.591
< 0.1%
255108.891
< 0.1%
231222.571
< 0.1%
221725.741
< 0.1%
219235.541
< 0.1%
213002.31
< 0.1%
199119.181
< 0.1%

total_family_members
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.157793007
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:15.380351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9107661149
Coefficient of variation (CV)0.4220822443
Kurtosis1.343656563
Mean2.157793007
Median Absolute Deviation (MAD)0
Skewness0.9265318712
Sum98240
Variance0.829494916
MonotonicityNot monotonic
2023-01-04T21:53:15.427975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
223538
51.7%
19913
21.8%
37812
 
17.2%
43623
 
8.0%
5564
 
1.2%
657
 
0.1%
712
 
< 0.1%
86
 
< 0.1%
102
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
19913
21.8%
223538
51.7%
37812
 
17.2%
43623
 
8.0%
5564
 
1.2%
657
 
0.1%
712
 
< 0.1%
86
 
< 0.1%
91
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
102
 
< 0.1%
91
 
< 0.1%
86
 
< 0.1%
712
 
< 0.1%
657
 
0.1%
5564
 
1.2%
43623
 
8.0%
37812
 
17.2%
223538
51.7%
19913
21.8%

prev_defaults
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 KiB
0
43060 
1
 
2172
2
 
296

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
043060
94.6%
12172
 
4.8%
2296
 
0.7%

Length

2023-01-04T21:53:15.480381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-04T21:53:15.517026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
043060
94.6%
12172
 
4.8%
2296
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

owns_house
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
True
31642 
False
13886 
ValueCountFrequency (%)
True31642
69.5%
False13886
30.5%
2023-01-04T21:53:15.540014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

owns_car
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
False
30290 
True
15238 
ValueCountFrequency (%)
False30290
66.5%
True15238
33.5%
2023-01-04T21:53:15.560393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

occupation_type
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 KiB
Unknown
14299 
Laborers
8134 
Sales staff
4725 
Core staff
4062 
Managers
3168 
Other values (14)
11140 

Length

Max length21
Median length8
Mean length9.444605517
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowLaborers
3rd rowLaborers
4th rowCore staff
5th rowCore staff

Common Values

ValueCountFrequency (%)
Unknown14299
31.4%
Laborers8134
17.9%
Sales staff4725
 
10.4%
Core staff4062
 
8.9%
Managers3168
 
7.0%
Drivers2747
 
6.0%
High skill tech staff1682
 
3.7%
Accountants1474
 
3.2%
Medicine staff1275
 
2.8%
Security staff1025
 
2.3%
Other values (9)2937
 
6.5%

Length

2023-01-04T21:53:15.603540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
staff15070
23.3%
unknown14299
22.1%
laborers8470
13.1%
sales4725
 
7.3%
core4062
 
6.3%
managers3168
 
4.9%
drivers2747
 
4.2%
high1682
 
2.6%
skill1682
 
2.6%
tech1682
 
2.6%
Other values (14)7199
11.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

no_of_days_employed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7876
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67984.95158
Minimum2
Maximum365252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:15.671010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile228.35
Q1946.75
median2208.5
Q35840
95-th percentile365249
Maximum365252
Range365250
Interquartile range (IQR)4893.25

Descriptive statistics

Standard deviation139642.703
Coefficient of variation (CV)2.054023718
Kurtosis0.7520071558
Mean67984.95158
Median Absolute Deviation (MAD)1591.5
Skewness1.658444095
Sum3095218876
Variance1.95000845 × 1010
MonotonicityNot monotonic
2023-01-04T21:53:15.745639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365241759
 
1.7%
365246684
 
1.5%
365244669
 
1.5%
365240641
 
1.4%
365245631
 
1.4%
365247625
 
1.4%
365250609
 
1.3%
365243607
 
1.3%
365251607
 
1.3%
365252602
 
1.3%
Other values (7866)39094
85.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
61
< 0.1%
121
< 0.1%
131
< 0.1%
172
< 0.1%
211
< 0.1%
231
< 0.1%
241
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
365252602
1.3%
365251607
1.3%
365250609
1.3%
365249601
1.3%
365248594
1.3%
365247625
1.4%
365246684
1.5%
365245631
1.4%
365244669
1.5%
365243607
1.3%

no_of_children
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4135037779
Minimum0
Maximum9
Zeros32015
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:15.814495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7199722546
Coefficient of variation (CV)1.741150367
Kurtosis3.929285173
Mean0.4135037779
Median Absolute Deviation (MAD)0
Skewness1.853999638
Sum18826
Variance0.5183600474
MonotonicityNot monotonic
2023-01-04T21:53:15.862156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
032015
70.3%
18985
 
19.7%
23862
 
8.5%
3584
 
1.3%
460
 
0.1%
513
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
032015
70.3%
18985
 
19.7%
23862
 
8.5%
3584
 
1.3%
460
 
0.1%
513
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
66
 
< 0.1%
513
 
< 0.1%
460
 
0.1%
3584
 
1.3%
23862
 
8.5%
18985
 
19.7%
032015
70.3%

net_yearly_income
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct45502
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200655.6222
Minimum27170.61
Maximum140759012.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:15.927596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27170.61
5-th percentile77893.5085
Q1126345.835
median171714.91
Q3240603.76
95-th percentile392349.715
Maximum140759012.7
Range140731842.1
Interquartile range (IQR)114257.925

Descriptive statistics

Standard deviation669074.0346
Coefficient of variation (CV)3.334439509
Kurtosis42784.72019
Mean200655.6222
Median Absolute Deviation (MAD)54040.965
Skewness203.6835038
Sum9135449170
Variance4.476600638 × 1011
MonotonicityNot monotonic
2023-01-04T21:53:15.999898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174102.442
 
< 0.1%
228141.822
 
< 0.1%
130685.662
 
< 0.1%
136189.922
 
< 0.1%
1289152
 
< 0.1%
249134.472
 
< 0.1%
274014.392
 
< 0.1%
110317.42
 
< 0.1%
151482.742
 
< 0.1%
100156.162
 
< 0.1%
Other values (45492)45508
> 99.9%
ValueCountFrequency (%)
27170.611
< 0.1%
28532.171
< 0.1%
29191.131
< 0.1%
29453.341
< 0.1%
30176.761
< 0.1%
30270.451
< 0.1%
30393.011
< 0.1%
31261.551
< 0.1%
31489.641
< 0.1%
31750.011
< 0.1%
ValueCountFrequency (%)
140759012.71
< 0.1%
4433825.021
< 0.1%
4193101.771
< 0.1%
2784729.581
< 0.1%
2451896.421
< 0.1%
2413494.721
< 0.1%
2408259.111
< 0.1%
2217660.821
< 0.1%
2091355.911
< 0.1%
1947528.141
< 0.1%

migrant_worker
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 KiB
0.0
37389 
1.0
8139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.037389
82.1%
1.08139
 
17.9%

Length

2023-01-04T21:53:16.066914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-04T21:53:16.102487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.037389
82.1%
1.08139
 
17.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 KiB
F
29957 
M
15570 
XNA
 
1

Length

Max length3
Median length1
Mean length1.000043929
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F29957
65.8%
M15570
34.2%
XNA1
 
< 0.1%

Length

2023-01-04T21:53:16.141975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-04T21:53:16.184019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
f29957
65.8%
m15570
34.2%
xna1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

default_in_last_6months
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 KiB
0
43227 
1
 
2301

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
043227
94.9%
12301
 
5.1%

Length

2023-01-04T21:53:16.222095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-04T21:53:16.256898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
043227
94.9%
12301
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct45528
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size355.8 KiB
CST_115179
 
1
CST_115723
 
1
CST_156690
 
1
CST_136647
 
1
CST_120077
 
1
Other values (45523)
45523 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45528 ?
Unique (%)100.0%

Sample

1st rowCST_115179
2nd rowCST_121920
3rd rowCST_109330
4th rowCST_128288
5th rowCST_151355

Common Values

ValueCountFrequency (%)
CST_1151791
 
< 0.1%
CST_1157231
 
< 0.1%
CST_1566901
 
< 0.1%
CST_1366471
 
< 0.1%
CST_1200771
 
< 0.1%
CST_1233821
 
< 0.1%
CST_1346901
 
< 0.1%
CST_1340801
 
< 0.1%
CST_1228921
 
< 0.1%
CST_1276521
 
< 0.1%
Other values (45518)45518
> 99.9%

Length

2023-01-04T21:53:16.293080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cst_1151791
 
< 0.1%
cst_1459991
 
< 0.1%
cst_1513321
 
< 0.1%
cst_1537731
 
< 0.1%
cst_1093301
 
< 0.1%
cst_1282881
 
< 0.1%
cst_1513551
 
< 0.1%
cst_1232681
 
< 0.1%
cst_1275021
 
< 0.1%
cst_1517221
 
< 0.1%
Other values (45518)45518
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

credit_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct450
Distinct (%)1.0%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean782.7912566
Minimum500
Maximum949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:16.356276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile621.95
Q1704
median786
Q3867
95-th percentile933
Maximum949
Range449
Interquartile range (IQR)163

Descriptive statistics

Standard deviation100.6197458
Coefficient of variation (CV)0.1285396904
Kurtosis-0.5473691428
Mean782.7912566
Median Absolute Deviation (MAD)81.5
Skewness-0.3025168255
Sum35632658
Variance10124.33323
MonotonicityNot monotonic
2023-01-04T21:53:16.431530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670186
 
0.4%
660183
 
0.4%
684176
 
0.4%
682175
 
0.4%
651175
 
0.4%
662174
 
0.4%
681172
 
0.4%
691171
 
0.4%
692169
 
0.4%
659168
 
0.4%
Other values (440)43771
96.1%
ValueCountFrequency (%)
50011
< 0.1%
50119
< 0.1%
50221
< 0.1%
50318
< 0.1%
50411
< 0.1%
50517
< 0.1%
50620
< 0.1%
50714
< 0.1%
50820
< 0.1%
50919
< 0.1%
ValueCountFrequency (%)
949146
0.3%
948151
0.3%
947131
0.3%
946138
0.3%
945123
0.3%
944126
0.3%
943146
0.3%
942148
0.3%
941151
0.3%
940139
0.3%

credit_limit_used(%)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.23502021
Minimum0
Maximum99
Zeros430
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:16.508249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q127
median54
Q378
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.3769096
Coefficient of variation (CV)0.562398741
Kurtosis-1.243307626
Mean52.23502021
Median Absolute Deviation (MAD)26
Skewness-0.1274493369
Sum2378156
Variance863.0028178
MonotonicityNot monotonic
2023-01-04T21:53:16.583208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90584
 
1.3%
81571
 
1.3%
87569
 
1.2%
89568
 
1.2%
84566
 
1.2%
78564
 
1.2%
72558
 
1.2%
80557
 
1.2%
99556
 
1.2%
85554
 
1.2%
Other values (90)39881
87.6%
ValueCountFrequency (%)
0430
0.9%
1389
0.9%
2433
1.0%
3431
0.9%
4411
0.9%
5439
1.0%
6437
1.0%
7423
0.9%
8429
0.9%
9425
0.9%
ValueCountFrequency (%)
99556
1.2%
98513
1.1%
97540
1.2%
96524
1.2%
95522
1.1%
94472
1.0%
93507
1.1%
92549
1.2%
91513
1.1%
90584
1.3%

credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct45371
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43548.41603
Minimum4003.14
Maximum31129970.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:16.746774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4003.14
5-th percentile13356.867
Q123973.805
median35688.045
Q353435.7625
95-th percentile94836.2125
Maximum31129970.49
Range31125967.35
Interquartile range (IQR)29461.9575

Descriptive statistics

Standard deviation148784.6869
Coefficient of variation (CV)3.416534984
Kurtosis41860.88255
Mean43548.41603
Median Absolute Deviation (MAD)13778.625
Skewness200.3871671
Sum1982672285
Variance2.213688305 × 1010
MonotonicityNot monotonic
2023-01-04T21:53:16.819511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33429.222
 
< 0.1%
27557.562
 
< 0.1%
42343.392
 
< 0.1%
47321.452
 
< 0.1%
20353.392
 
< 0.1%
24673.12
 
< 0.1%
17772.022
 
< 0.1%
37674.22
 
< 0.1%
21793.672
 
< 0.1%
44933.962
 
< 0.1%
Other values (45361)45508
> 99.9%
ValueCountFrequency (%)
4003.141
< 0.1%
4030.681
< 0.1%
4042.771
< 0.1%
4210.961
< 0.1%
4219.321
< 0.1%
4328.611
< 0.1%
4328.991
< 0.1%
4356.151
< 0.1%
4483.551
< 0.1%
4601.31
< 0.1%
ValueCountFrequency (%)
31129970.491
< 0.1%
1015611.881
< 0.1%
841596.191
< 0.1%
817713.831
< 0.1%
648100.991
< 0.1%
621953.641
< 0.1%
610931.71
< 0.1%
580567.351
< 0.1%
548115.451
< 0.1%
477006.231
< 0.1%

credit_card_default
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 KiB
0
41831 
1
 
3697

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
041831
91.9%
13697
 
8.1%

Length

2023-01-04T21:53:16.885034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-04T21:53:16.920542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
041831
91.9%
13697
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.99341065
Minimum23
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2023-01-04T21:53:16.959210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile24
Q131
median39
Q347
95-th percentile54
Maximum55
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.543990289
Coefficient of variation (CV)0.2447590537
Kurtosis-1.203387195
Mean38.99341065
Median Absolute Deviation (MAD)8
Skewness0.003974890882
Sum1775292
Variance91.08775064
MonotonicityNot monotonic
2023-01-04T21:53:17.022164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
401455
 
3.2%
551448
 
3.2%
361420
 
3.1%
231411
 
3.1%
351410
 
3.1%
371409
 
3.1%
491408
 
3.1%
261399
 
3.1%
481396
 
3.1%
271395
 
3.1%
Other values (23)31377
68.9%
ValueCountFrequency (%)
231411
3.1%
241369
3.0%
251373
3.0%
261399
3.1%
271395
3.1%
281368
3.0%
291395
3.1%
301384
3.0%
311373
3.0%
321380
3.0%
ValueCountFrequency (%)
551448
3.2%
541383
3.0%
531385
3.0%
521345
3.0%
511371
3.0%
501377
3.0%
491408
3.1%
481396
3.1%
471314
2.9%
461364
3.0%

Interactions

2023-01-04T21:53:13.803742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:08.409545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.181025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.797379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.459074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.164730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.820527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.442670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.158673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.874717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:08.495517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.252780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.875062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.529249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.237308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.889903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.513848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.232194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.941420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:08.566410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.316584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.946195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.593367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.305451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.954561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.580402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.301043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:14.014465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:08.648115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.387425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.021273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.663876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.382702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.027315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.655243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.379333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:14.080070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:08.717377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.453799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.090039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.725908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.450785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.091941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.720441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.444916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:14.153035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:08.794304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.529167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.166301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.797594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.527550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.165681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.795227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.518482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:14.218628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:08.865055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.594302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.237113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.868233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.597096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.236501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.862035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.592262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:14.286562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.036520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.660629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.309754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.934066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.674205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.304371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.929738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.661628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:14.393504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.109813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:09.730528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:10.385588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.098183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:11.748305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:12.374844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.000336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T21:53:13.732955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-04T21:53:17.088832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-04T21:53:17.205893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-04T21:53:17.323290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-04T21:53:17.433900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-04T21:53:17.525089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-04T21:53:14.517616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-04T21:53:14.745566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-04T21:53:14.941539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-04T21:53:15.006221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

customer_idagegenderowns_carowns_houseno_of_childrennet_yearly_incomeno_of_days_employedoccupation_typetotal_family_membersmigrant_workeryearly_debt_paymentscredit_limitcredit_limit_used(%)credit_scoreprev_defaultsdefault_in_last_6monthscredit_card_default
0CST_11517946FNY0.0107934.04612.0Unknown1.01.033070.2818690.9373544.0211
1CST_12192029MNY0.0109862.622771.0Laborers2.00.015329.5337745.1952857.0000
2CST_10933037MNY0.0230153.17204.0Laborers2.00.048416.6041598.3643650.0000
3CST_12828839FNY0.0122325.8211941.0Core staff2.00.022574.3632627.7620754.0000
4CST_15135546MYY0.0387286.001459.0Core staff1.00.038282.9552950.6475927.0000
5CST_12326846FYN0.0252765.912898.0Accountants2.01.037046.8640245.6419937.0000
6CST_12750238MNY1.0262389.205541.0High skill tech staff3.00.050839.3941311.0842733.0000
7CST_15172246FYY1.0241211.391448.0Core staff3.00.030008.4632209.2291906.0000
8CST_13376840FNY0.0210091.4311551.0Laborers2.00.021521.8965037.7414783.0000
9CST_11167039FYY2.0207109.132791.0High skill tech staff4.00.09509.1028425.5214666.0000

Last rows

customer_idagegenderowns_carowns_houseno_of_childrennet_yearly_incomeno_of_days_employedoccupation_typetotal_family_membersmigrant_workeryearly_debt_paymentscredit_limitcredit_limit_used(%)credit_scoreprev_defaultsdefault_in_last_6monthscredit_card_default
45518CST_11248154FNY2.0252941.682266.0Laborers3.00.037513.3845775.4371677.0001
45519CST_14454441MYY2.0293494.24794.0Core staff4.00.040402.3783883.3797665.0000
45520CST_13796647MYY0.0291628.761677.0Drivers1.00.015627.7342980.8285915.0000
45521CST_16106848FNY0.089435.47365249.0Unknown2.00.031233.8821850.7736879.0000
45522CST_12054554FNN1.0138001.12161.0Unknown3.00.016609.0418565.6371893.0000
45523CST_13042155FNN2.096207.57117.0Unknown4.00.011229.5429663.8382907.0000
45524CST_13667031FNY0.0383476.74966.0Accountants2.01.043369.91139947.1632679.0000
45525CST_14543527FNY0.0260052.181420.0Core staff2.00.022707.5183961.8346727.0000
45526CST_13091332MYN0.0157363.042457.0Laborers2.00.020150.1025538.7292805.0000
45527CST_16007838MNY1.0316896.281210.0Unknown3.00.034603.7836630.7626682.0000